Research in Engineering Design

, Volume 25, Issue 3, pp 223–239 | Cite as

Solving complex design problems through multiobjective optimisation taking into account judgements of users

Original Paper

Abstract

In the design process of products or systems, a current trend consists in taking into account judgments of users. In this context, a multiobjective optimisation method taking into account judgments of a panel of subjects is proposed. It is aimed at identifying the best trade-offs between quantitative objectives and judgments of users. The method is divided in two steps: (1) judgment data acquisition and (2) integration of the judgment data into the multiobjective optimisation process. The method is based on a stochastic Pareto-based evolutionary algorithm for optimisation and on a multilinear interpolation for judgment modelling. The combination of these techniques makes it possible to solve complex problems, with up to eight decision variables and up to at least eight objectives. Relevant applications of the method include optimisation with judgments about various aspects of the product or system, identification of the best trade-offs satisfying at the same time several groups with different judgments, and analysis of the interest of market segmentation. For illustration purpose, a pilot study about an individual office lighting design problem is processed.

Keywords

Design problem Judgment modelling Multiobjective optimisation Decision-making 

References

  1. Avigad G, Matalon EE (2011) The multi-single-objective problem and its solution by way of evolutionary algorithms. Res Eng Des 22:87–102CrossRefGoogle Scholar
  2. Bell DE (1979) Multiattribute utility functions: decompositions using interpolation. Manag Sci 25(8):744–753CrossRefMATHGoogle Scholar
  3. Birnbaum MH (1974) Using contextual effects to derive psychophysical Scales. Atten Percept Psychophys 15(1):89–96CrossRefMathSciNetGoogle Scholar
  4. Boyce P, Veitch J, Newsham G, Jones C, Heerwagen J, Myer M et al (2006) Lighting quality and office work: two field simulation experiments. Light Res Technol 38(3):191CrossRefGoogle Scholar
  5. Branke J, Deb K, Miettinen K, Slowinski R (2008) Multiobjective optimisation: interactive and evolutionary approaches. Springer, BerlinCrossRefGoogle Scholar
  6. Brintrup AM, Ramsden J, Tiwari A (2007) An interactive genetic algorithm-based framework for handling qualitative criteria in design optimization. J Comput Ind 58(3):279–291CrossRefGoogle Scholar
  7. Brintrup AM, Ramsden J, Takagi H, Tiwari A (2008) Ergonomic chair design by fusing qualitative and quantitative criteria using interactive genetic algorithms. IEEE Trans Evol Comput 12(3):343–354CrossRefGoogle Scholar
  8. Coello Coello AC, Lamont GB, Van Veldhuizen DA (2007) Evolutionary algorithms for solving multi-objective problems. Springer, New YorkMATHGoogle Scholar
  9. Curtis SK, Hancock BJ, Mattson CA (2013) Usage scenarios for design space exploration with a dynamic multiobjective optimization formulation. Res Eng Des 24:395–409CrossRefGoogle Scholar
  10. Das I, Dennis JE (1997) A closer look at drawbacks of minimizing weighted sums of objectives for Pareto set generation in multicriteria optimization problems. Struct Optim 14(1):63–69CrossRefGoogle Scholar
  11. De Graaf C, Van Staveren WA, Burema J (1996) Psychophysical and psychohedonic functions of four common food flavours in elderly subjects. Chem Senses 21(3):293–302CrossRefGoogle Scholar
  12. De Jong N, De Graaf C, Van Staveren WA (1996) Effect of sucrose in breakfast items on pleasantness and food intake in the elderly. Physiol Behav 60(6):1453–1462CrossRefGoogle Scholar
  13. Deb K, Agrawal RB (1994) Simulated binary crossover for continuous search space. Complex Syst 1(9):115–148MathSciNetGoogle Scholar
  14. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197CrossRefGoogle Scholar
  15. Fung RYK, Tang J, Tu PY, Chen Y (2003) Modelling of quality function deployment planning with resource allocation. Res Eng Des 14:247–255CrossRefGoogle Scholar
  16. Geyer P (2008) Multidisciplinary grammars supporting design optimization of buildings. Res Eng Des 18:197–216CrossRefGoogle Scholar
  17. Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addision-Wesley Professional, BostonMATHGoogle Scholar
  18. Gordon AD (1999) Classification, 2nd edn. Chapman and Hall, LondresMATHGoogle Scholar
  19. Huber J (1976) Ideal point models of preference. Adv Consum Res 3:138–142Google Scholar
  20. Inoue M, Takagi H (2008) Layout algorithm for an EC-based room layout planning support system. Paper presented at the IEEE conference on soft computing in industrial applications, 165–170Google Scholar
  21. Ishibuchi H, Tsukamoto N, Nojima Y (2008) Evolutionary many-objective optimization: a short review. Paper presented at the IEEE congress on evolutionary computation, 2424–2431Google Scholar
  22. Johnsen K, Rasmussen HF, Iversen A, Fischer C, Larsen CPV, Traberg-Borup S (2009) Kontorbelysning baseret pa energieffektive arbejdslamper, Report SBI 2009:09 Statens Byggeforskningsinstitut. Aalborg Universitet, DenmarkGoogle Scholar
  23. Keeney RL, Raiffa H (1993) Decisions with multiple objectives preferences and value tradeoffs. Cambridge University Press, CambridgeGoogle Scholar
  24. Kelly JC, Maheut P, Petiot JF, Papalambros PY (2011) Incorporating user shape preference in engineering design optimisation. J Eng Des 22(9):627–650CrossRefGoogle Scholar
  25. Lim J (2011) Hedonic scaling: a review of methods and theory. Food Qual Prefer 22(8):733–747Google Scholar
  26. Liu Y (2003) The aesthetic and the ethic dimensions of human factors and design. Ergonomics 46(13–14):1293–1305CrossRefGoogle Scholar
  27. Machwe A, Parmee IC (2006) Integrating aesthetic criteria with evolutionary processes in complex, free-form design—an initial investigation. Paper presented in the IEEE congress on evolutionary computation, 165–172Google Scholar
  28. Machwe A, Parmee IC, Miles JC (2005) Integrating aesthetic criteria with a user-centric evolutionary system via a component based design representation. Paper presented at the international conference on engineering design ICED 5:15–18Google Scholar
  29. Moon SK, Park KJ, Simpson TW (2013) Platform design variable identification for a product family using multi-objective particle swarm optimization. Res Eng Des 1–14. doi:10.1007/s00163-013-0166-0
  30. Nagamachi M (1995) Kansei engineering: a new ergonomic consumer oriented technology for product development. Int J Ind Ergon 15:3–11CrossRefGoogle Scholar
  31. Nakache JP, Confais J (2004) Approche pragmatique de la classification: arbres hiérarchiques, partitionnements. Editions Technip, ParisGoogle Scholar
  32. Orsborn S, Cagnan J, Boatwright P (2009) Quantifying aesthetic form preference in a utility function. J Mech Des 131(6):61001-1–61001-10Google Scholar
  33. Palm R (2002) Utilisation du bootstrap pour les problèmes statistiques lies à l’estimation des paramètres. Biotechnologie agronomie société et environnement 6(3):143–154MathSciNetGoogle Scholar
  34. Parducci A, Wedell DH (1986) The category effect with rating scales: number of categories, number of stimuli, and method of presentation. J Exp Psychol Hum Percept Perform 12(4):496–516CrossRefGoogle Scholar
  35. Petiot J-F, Grognet S (2006) Product design: a vectors field-based approach for preference modelling. J Eng Des 17(3):217–233CrossRefGoogle Scholar
  36. Poirson E, Petiot JF, Aliouat E, Boivin L, Blumenthal D (2010) Interactive user tests to enhance innovation. Application to car dashboard design. Paper presented in the international conference on Kansei engineering and emotion research, Paris, France, 2021–2030Google Scholar
  37. Ramaswamy R, Ulrich K (1993) Augmenting the House of Quality with engineering models. Res Eng Des 5:70–79CrossRefGoogle Scholar
  38. Shibuya M, Kita H, Kobayashi S (1999) Integration of multi-objective and interactive genetic algorithms and its application to animation design. Paper presented at the IEEE systems, man and cybernetics (SMC) conference, Tokyo, JapanGoogle Scholar
  39. Takagi H (2001) Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation. In: Proceedings of the IEEEGoogle Scholar
  40. Takagi H (2009) New IEC research and frameworks. In: Aspects of soft computing, intelligent robotics and control studies in computational intelligence, vol 241, pp 65–76Google Scholar
  41. Thurstone LL (1927) A law of comparative judgment. Psychol Rev 34(4):273–286CrossRefGoogle Scholar
  42. Tocher KD (1952) The design and analysis of block experiments. J Roy Stat Soc: Ser B (Methodol) 14:45–100MATHMathSciNetGoogle Scholar
  43. Van de Poel I (2007) Methodological problems in QFD and directions for future development. Res Eng Des 18:21–36CrossRefGoogle Scholar
  44. Wakeling IN, Buck D (2001) Balanced incomplete block designs useful for consumer experimentation. Food Qual Prefer 12(4):265–268CrossRefGoogle Scholar
  45. Yamakawa K, Watabe K, Inanuma M, Sakata K, Takeda H (2000) A study on the practical use of a task and ambient lighting system in an office. J Light Vis Environ 24(2):15–18CrossRefGoogle Scholar
  46. Zandstra EH, De Graaf C, Van Trijp HCM, Van Staveren WA (1999) Laboratory hedonic ratings as predictors of consumption. Food Qual Prefer 10(4–5):411–418CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  1. 1.IFSTTAR, COSYS, LEPSISChamps-sur-MarneFrance
  2. 2.Université de LyonLyonFrance
  3. 3.Ecole Nationale des Travaux Publics de l’Etat, Département Génie Civil BâtimentVaulx-en-VelinFrance

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